Overview

Dataset statistics

Number of variables4
Number of observations726
Missing cells384
Missing cells (%)13.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.8 KiB
Average record size in memory32.2 B

Variable types

Categorical1
Text3

Dataset

Description서울특별시_송파구_체육시설에 대한 데이터로 업종, 상호, 시설주소(도로명), 시설전화번호 등에 항목으로 제공합니다.
URLhttps://www.data.go.kr/data/15005433/fileData.do

Alerts

시설주소(도로명) has 11 (1.5%) missing valuesMissing
시설전화번호 has 373 (51.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:34:27.048330
Analysis finished2023-12-12 10:34:28.246349
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct13
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
체력단련장업
217 
체육도장업
150 
당구장업
146 
골프연습장업
129 
가상체험 체육시설업
37 
Other values (8)
47 

Length

Max length10
Median length7
Mean length5.476584
Min length2

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row수영장업
2nd row수영장업
3rd row수영장업
4th row수영장업
5th row수영장업

Common Values

ValueCountFrequency (%)
체력단련장업 217
29.9%
체육도장업 150
20.7%
당구장업 146
20.1%
골프연습장업 129
17.8%
가상체험 체육시설업 37
 
5.1%
수영장업 13
 
1.8%
기타 12
 
1.7%
체육교습업 9
 
1.2%
종합체육시설업 8
 
1.1%
볼링장 2
 
0.3%
Other values (3) 3
 
0.4%

Length

2023-12-12T19:34:28.441746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
체력단련장업 217
28.4%
체육도장업 150
19.7%
당구장업 146
19.1%
골프연습장업 129
16.9%
가상체험 37
 
4.8%
체육시설업 37
 
4.8%
수영장업 13
 
1.7%
기타 12
 
1.6%
체육교습업 9
 
1.2%
종합체육시설업 8
 
1.0%
Other values (4) 5
 
0.7%

상호
Text

Distinct703
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2023-12-12T19:34:29.062326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length26
Mean length8.1363636
Min length2

Characters and Unicode

Total characters5907
Distinct characters475
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique682 ?
Unique (%)93.9%

Sample

1st row잠실실내수영장
2nd row롯데호텔 실내수영장
3rd row한강공원 잠실수영장
4th row아쿠아키즈
5th row에이치투오 어린이수영장
ValueCountFrequency (%)
당구장 21
 
1.8%
휘트니스 18
 
1.6%
태권도 17
 
1.5%
gym 16
 
1.4%
잠실점 11
 
1.0%
당구클럽 10
 
0.9%
골프존 10
 
0.9%
짐 9
 
0.8%
스크린골프 8
 
0.7%
체육관 8
 
0.7%
Other values (844) 1029
88.9%
2023-12-12T19:34:29.744560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
431
 
7.3%
스 283
 
4.8%
프 157
 
2.7%
장 147
 
2.5%
골 143
 
2.4%
구 135
 
2.3%
당 130
 
2.2%
이 118
 
2.0%
도 104
 
1.8%
트 98
 
1.7%
Other values (465) 4161
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4634
78.4%
Space Separator 431
 
7.3%
Uppercase Letter 407
 
6.9%
Lowercase Letter 255
 
4.3%
Decimal Number 52
 
0.9%
Open Punctuation 49
 
0.8%
Close Punctuation 49
 
0.8%
Other Punctuation 25
 
0.4%
Dash Punctuation 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 283
 
6.1%
프 157
 
3.4%
장 147
 
3.2%
골 143
 
3.1%
구 135
 
2.9%
당 130
 
2.8%
이 118
 
2.5%
도 104
 
2.2%
트 98
 
2.1%
권 82
 
1.8%
Other values (402) 3237
69.9%
Uppercase Letter
ValueCountFrequency (%)
T 40
 
9.8%
G 37
 
9.1%
S 31
 
7.6%
P 28
 
6.9%
M 27
 
6.6%
Y 26
 
6.4%
A 19
 
4.7%
B 18
 
4.4%
J 17
 
4.2%
C 16
 
3.9%
Other values (13) 148
36.4%
Lowercase Letter
ValueCountFrequency (%)
e 36
14.1%
s 26
10.2%
i 25
9.8%
t 22
 
8.6%
n 17
 
6.7%
o 17
 
6.7%
a 16
 
6.3%
r 16
 
6.3%
y 14
 
5.5%
l 11
 
4.3%
Other values (11) 55
21.6%
Decimal Number
ValueCountFrequency (%)
2 14
26.9%
0 11
21.2%
3 7
13.5%
1 6
11.5%
9 4
 
7.7%
5 4
 
7.7%
4 3
 
5.8%
8 1
 
1.9%
7 1
 
1.9%
6 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
& 10
40.0%
. 9
36.0%
' 4
 
16.0%
" 2
 
8.0%
Space Separator
ValueCountFrequency (%)
431
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4634
78.4%
Latin 662
 
11.2%
Common 611
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 283
 
6.1%
프 157
 
3.4%
장 147
 
3.2%
골 143
 
3.1%
구 135
 
2.9%
당 130
 
2.8%
이 118
 
2.5%
도 104
 
2.2%
트 98
 
2.1%
권 82
 
1.8%
Other values (402) 3237
69.9%
Latin
ValueCountFrequency (%)
T 40
 
6.0%
G 37
 
5.6%
e 36
 
5.4%
S 31
 
4.7%
P 28
 
4.2%
M 27
 
4.1%
s 26
 
3.9%
Y 26
 
3.9%
i 25
 
3.8%
t 22
 
3.3%
Other values (34) 364
55.0%
Common
ValueCountFrequency (%)
431
70.5%
( 49
 
8.0%
) 49
 
8.0%
2 14
 
2.3%
0 11
 
1.8%
& 10
 
1.6%
. 9
 
1.5%
3 7
 
1.1%
1 6
 
1.0%
9 4
 
0.7%
Other values (9) 21
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4634
78.4%
ASCII 1273
 
21.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
431
33.9%
( 49
 
3.8%
) 49
 
3.8%
T 40
 
3.1%
G 37
 
2.9%
e 36
 
2.8%
S 31
 
2.4%
P 28
 
2.2%
M 27
 
2.1%
s 26
 
2.0%
Other values (53) 519
40.8%
Hangul
ValueCountFrequency (%)
스 283
 
6.1%
프 157
 
3.4%
장 147
 
3.2%
골 143
 
3.1%
구 135
 
2.9%
당 130
 
2.8%
이 118
 
2.5%
도 104
 
2.2%
트 98
 
2.1%
권 82
 
1.8%
Other values (402) 3237
69.9%

시설주소(도로명)
Text

MISSING 

Distinct684
Distinct (%)95.7%
Missing11
Missing (%)1.5%
Memory size5.8 KiB
2023-12-12T19:34:30.123078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length50
Mean length31.516084
Min length22

Characters and Unicode

Total characters22534
Distinct characters294
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique656 ?
Unique (%)91.7%

Sample

1st row서울특별시 송파구 올림픽로 25 (잠실동, 종합운동장)
2nd row서울특별시 송파구 올림픽로 240, 5층 (잠실동, 롯데호텔)
3rd row서울특별시 송파구 한가람로 65 (잠실동, 한강사업본부 잠실안내센터)
4th row서울특별시 송파구 삼전로 지하 95 (잠실동, 태성빌딩)
5th row서울특별시 송파구 오금로 233 (방이동)
ValueCountFrequency (%)
서울특별시 715
 
16.0%
송파구 715
 
16.0%
지하1층 127
 
2.9%
잠실동 106
 
2.4%
가락동 94
 
2.1%
문정동 88
 
2.0%
방이동 84
 
1.9%
2층 64
 
1.4%
3층 58
 
1.3%
송파동 55
 
1.2%
Other values (828) 2349
52.7%
2023-12-12T19:34:30.695360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3742
 
16.6%
파 881
 
3.9%
송 864
 
3.8%
동 796
 
3.5%
1 736
 
3.3%
시 728
 
3.2%
로 724
 
3.2%
( 719
 
3.2%
) 719
 
3.2%
울 718
 
3.2%
Other values (284) 11907
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13507
59.9%
Space Separator 3742
 
16.6%
Decimal Number 3046
 
13.5%
Open Punctuation 719
 
3.2%
Close Punctuation 719
 
3.2%
Other Punctuation 639
 
2.8%
Uppercase Letter 94
 
0.4%
Dash Punctuation 50
 
0.2%
Math Symbol 16
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 881
 
6.5%
송 864
 
6.4%
동 796
 
5.9%
시 728
 
5.4%
로 724
 
5.4%
울 718
 
5.3%
서 718
 
5.3%
구 715
 
5.3%
별 715
 
5.3%
특 715
 
5.3%
Other values (245) 5933
43.9%
Uppercase Letter
ValueCountFrequency (%)
B 31
33.0%
A 10
 
10.6%
M 5
 
5.3%
U 5
 
5.3%
K 4
 
4.3%
E 4
 
4.3%
G 4
 
4.3%
N 4
 
4.3%
F 3
 
3.2%
S 3
 
3.2%
Other values (10) 21
22.3%
Decimal Number
ValueCountFrequency (%)
1 736
24.2%
2 505
16.6%
3 360
11.8%
4 329
10.8%
0 272
 
8.9%
5 234
 
7.7%
8 181
 
5.9%
6 153
 
5.0%
7 142
 
4.7%
9 134
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 636
99.5%
. 3
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
c 1
50.0%
Space Separator
ValueCountFrequency (%)
3742
100.0%
Open Punctuation
ValueCountFrequency (%)
( 719
100.0%
Close Punctuation
ValueCountFrequency (%)
) 719
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13507
59.9%
Common 8931
39.6%
Latin 96
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 881
 
6.5%
송 864
 
6.4%
동 796
 
5.9%
시 728
 
5.4%
로 724
 
5.4%
울 718
 
5.3%
서 718
 
5.3%
구 715
 
5.3%
별 715
 
5.3%
특 715
 
5.3%
Other values (245) 5933
43.9%
Latin
ValueCountFrequency (%)
B 31
32.3%
A 10
 
10.4%
M 5
 
5.2%
U 5
 
5.2%
K 4
 
4.2%
E 4
 
4.2%
G 4
 
4.2%
N 4
 
4.2%
F 3
 
3.1%
S 3
 
3.1%
Other values (12) 23
24.0%
Common
ValueCountFrequency (%)
3742
41.9%
1 736
 
8.2%
( 719
 
8.1%
) 719
 
8.1%
, 636
 
7.1%
2 505
 
5.7%
3 360
 
4.0%
4 329
 
3.7%
0 272
 
3.0%
5 234
 
2.6%
Other values (7) 679
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13507
59.9%
ASCII 9027
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3742
41.5%
1 736
 
8.2%
( 719
 
8.0%
) 719
 
8.0%
, 636
 
7.0%
2 505
 
5.6%
3 360
 
4.0%
4 329
 
3.6%
0 272
 
3.0%
5 234
 
2.6%
Other values (29) 775
 
8.6%
Hangul
ValueCountFrequency (%)
파 881
 
6.5%
송 864
 
6.4%
동 796
 
5.9%
시 728
 
5.4%
로 724
 
5.4%
울 718
 
5.3%
서 718
 
5.3%
구 715
 
5.3%
별 715
 
5.3%
특 715
 
5.3%
Other values (245) 5933
43.9%

시설전화번호
Text

MISSING 

Distinct341
Distinct (%)96.6%
Missing373
Missing (%)51.4%
Memory size5.8 KiB
2023-12-12T19:34:31.115343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.206799
Min length9

Characters and Unicode

Total characters3956
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique331 ?
Unique (%)93.8%

Sample

1st row02-2240-8751
2nd row02-419-7000
3rd row02-417-7845
4th row02-470-1415
5th row02-2417-7555
ValueCountFrequency (%)
02-419-7000 3
 
0.8%
02-410-1626 3
 
0.8%
02-2047-3664 2
 
0.6%
02-488-4002 2
 
0.6%
02-409-0600 2
 
0.6%
02-2240-4600 2
 
0.6%
02-425-6555 2
 
0.6%
02-417-3367 2
 
0.6%
02-403-0753 2
 
0.6%
02-415-3070 2
 
0.6%
Other values (331) 331
93.8%
2023-12-12T19:34:32.155515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 724
18.3%
- 698
17.6%
2 617
15.6%
4 515
13.0%
1 286
 
7.2%
3 228
 
5.8%
8 200
 
5.1%
7 198
 
5.0%
9 178
 
4.5%
6 157
 
4.0%
Other values (2) 155
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3257
82.3%
Dash Punctuation 698
 
17.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 724
22.2%
2 617
18.9%
4 515
15.8%
1 286
 
8.8%
3 228
 
7.0%
8 200
 
6.1%
7 198
 
6.1%
9 178
 
5.5%
6 157
 
4.8%
5 154
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 698
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 724
18.3%
- 698
17.6%
2 617
15.6%
4 515
13.0%
1 286
 
7.2%
3 228
 
5.8%
8 200
 
5.1%
7 198
 
5.0%
9 178
 
4.5%
6 157
 
4.0%
Other values (2) 155
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 724
18.3%
- 698
17.6%
2 617
15.6%
4 515
13.0%
1 286
 
7.2%
3 228
 
5.8%
8 200
 
5.1%
7 198
 
5.0%
9 178
 
4.5%
6 157
 
4.0%
Other values (2) 155
 
3.9%

Missing values

2023-12-12T19:34:27.755465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:34:27.932464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T19:34:28.157168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종상호시설주소(도로명)시설전화번호
0수영장업잠실실내수영장서울특별시 송파구 올림픽로 25 (잠실동, 종합운동장)02-2240-8751
1수영장업롯데호텔 실내수영장서울특별시 송파구 올림픽로 240, 5층 (잠실동, 롯데호텔)02-419-7000
2수영장업한강공원 잠실수영장서울특별시 송파구 한가람로 65 (잠실동, 한강사업본부 잠실안내센터)<NA>
3수영장업아쿠아키즈서울특별시 송파구 삼전로 지하 95 (잠실동, 태성빌딩)02-417-7845
4수영장업에이치투오 어린이수영장서울특별시 송파구 오금로 233 (방이동)02-470-1415
5수영장업키즈마린 워터파크서울특별시 송파구 삼전로 81 (잠실동)<NA>
6수영장업(주)스위스키즈베어서울특별시 송파구 삼학사로 99 (삼전동)02-2417-7555
7수영장업더블유엘지 문정서울특별시 송파구 문정로 83 (문정동, 문정래미안아파트)02-407-1888
8수영장업키즈웨일즈서울특별시 송파구 동남로 207 (가락동)02-3012-0707
9수영장업엘에스(LS)서울특별시 송파구 토성로 78 (풍납동, 정민빌딩)<NA>
업종상호시설주소(도로명)시설전화번호
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717체육교습업점핑위즈 줄넘기 클럽서울특별시 송파구 위례광장로 199, 성희프라자 3층 306호 (장지동)02-401-6126
718체육교습업오바른짐서울특별시 송파구 백제고분로 270, 지하1층 (삼전동)02-415-5857
719체육교습업태승 축구아카데미서울특별시 송파구 오금로 457, 인창빌딩 지하1층 (거여동)<NA>
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723체육교습업누리다 움직임 연구소서울특별시 송파구 거마로 14, 청송빌딩 5층 (거여동)02-418-1218
724체육교습업와이엔제이 스포츠 아카데미서울특별시 송파구 송파대로36가길 24, 현암빌딩 지하1층층 (송파동)02-3401-0479
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